A Computational Model of Auditory Feature Extraction and Sound Classification

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Abstract

This thesis introduces a computer model that incorporates responses similar to
those found in the cochlea, in sub-corticai auditory processing, and in auditory
cortex. The principle aim of this work is to show that this can form the basis
for a biologically plausible mechanism of auditory stimulus classification. We will
show that this classification is robust to stimulus variation and time compression.
In addition, the response of the system is shown to support multiple, concurrent,
behaviourally relevant classifications of natural stimuli (speech).
The model incorporates transient enhancement, an ensemble of spectro -
temporal filters, and a simple measure analogous to the idea of visual salience
to produce a quasi-static description of the stimulus suitable either for classification
with an analogue artificial neural network or, using appropriate rate coding,
a classifier based on artificial spiking neurons. We also show that the spectotemporal
ensemble can be derived from a limited class of 'formative' stimuli, consistent
with a developmental interpretation of ensemble formation. In addition,
ensembles chosen on information theoretic grounds consist of filters with relatively
simple geometries, which is consistent with reports of responses in mammalian
thalamus and auditory cortex.
A powerful feature of this approach is that the ensemble response, from
which salient auditory events are identified, amounts to stimulus-ensemble driven
method of segmentation which respects the envelope of the stimulus, and leads
to a quasi-static representation of auditory events which is suitable for spike rate
coding.
We also present evidence that the encoded auditory events may form the
basis of a representation-of-similarity, or second order isomorphism, which implies
a representational space that respects similarity relationships between stimuli
including novel stimuli.

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All items in PEARL are protected by copyright law.
Author manuscripts deposited to comply with open access mandates are made available in accordance with
publisher policies. Please cite only the published version using the details provided on the item record or document.
In the absence of an open licence (e.g. Creative Commons), permissions for further reuse of content should be
sought from the publisher or author.